Autism genetic database (AGD): a comprehensive database including autism susceptibility gene-CNVs integrated with known noncoding RNAs and fragile sites

<p>Abstract</p> <p>Background</p> <p>Autism is a highly heritable complex neurodevelopmental disorder, therefore identifying its genetic basis has been challenging. To date, numerous susceptibility genes and chromosomal abnormalities have been reported in association with autism, but most discoveries either fail to be replicated or account for a small effect. Thus, in most cases the underlying causative genetic mechanisms are not fully understood. In the present work, the Autism Genetic Database (AGD) was developed as a literature-driven, web-based, and easy to access database designed with the aim of creating a comprehensive repository for all the currently reported genes and genomic copy number variations (CNVs) associated with autism in order to further facilitate the assessment of these autism susceptibility genetic factors.</p> <p>Description</p> <p>AGD is a relational database that organizes data resulting from exhaustive literature searches for reported susceptibility genes and CNVs associated with autism. Furthermore, genomic information about human fragile sites and noncoding RNAs was also downloaded and parsed from miRBase, snoRNA-LBME-db, piRNABank, and the MIT/ICBP siRNA database. A web client genome browser enables viewing of the features while a web client query tool provides access to more specific information for the features. When applicable, links to external databases including GenBank, PubMed, miRBase, snoRNA-LBME-db, piRNABank, and the MIT siRNA database are provided.</p> <p>Conclusion</p> <p>AGD comprises a comprehensive list of susceptibility genes and copy number variations reported to-date in association with autism, as well as all known human noncoding RNA genes and fragile sites. Such a unique and inclusive autism genetic database will facilitate the evaluation of autism susceptibility factors in relation to known human noncoding RNAs and fragile sites, impacting on human diseases. As a result, this new autism database offers a valuable tool for the research community to evaluate genetic findings for this complex multifactorial disorder in an integrated format. AGD provides a genome browser and a web based query client for conveniently selecting features of interest. Access to AGD is freely available at <url>http://wren.bcf.ku.edu/</url>.</p

Digital Genome-Wide ncRNA Expression, Including SnoRNAs, across 11 Human Tissues Using PolyA-Neutral Amplification

Non-coding RNAs (ncRNAs) are an essential class of molecular species that have been difficult to monitor on high throughput platforms due to frequent lack of polyadenylation. Using a polyadenylation-neutral amplification protocol and next-generation sequencing, we explore ncRNA expression in eleven human tissues. ncRNAs 7SL, U2, 7SK, and HBII-52 are expressed at levels far exceeding mRNAs. C/D and H/ACA box snoRNAs are associated with rRNA methylation and pseudouridylation, respectively: spleen expresses both, hypothalamus expresses mainly C/D box snoRNAs, and testes show enriched expression of both H/ACA box snoRNAs and RNA telomerase TERC. Within the snoRNA 14q cluster, 14q(I-6) is expressed at much higher levels than other cluster members. More reads align to mitochondrial than nuclear tRNAs. Many lincRNAs are actively transcribed, particularly those overlapping known ncRNAs. Within the Prader-Willi syndrome loci, the snoRNA HBII-85 (group I) cluster is highly expressed in hypothalamus, greater than in other tissues and greater than group II or III. Additionally, within the disease locus we find novel transcription across a 400,000 nt span in ovaries. This genome-wide polyA-neutral expression compendium demonstrates the richness of ncRNA expression, their high expression patterns, their function-specific expression patterns, and is publicly available

Characterization of RNase MRP RNA and novel snoRNAs from Giardia intestinalis and Trichomonas vaginalis

<p>Abstract</p> <p>Background</p> <p>Eukaryotic cells possess a complex network of RNA machineries which function in RNA-processing and cellular regulation which includes transcription, translation, silencing, editing and epigenetic control. Studies of model organisms have shown that many ncRNAs of the RNA-infrastructure are highly conserved, but little is known from non-model protists. In this study we have conducted a genome-scale survey of medium-length ncRNAs from the protozoan parasites <it>Giardia intestinalis </it>and <it>Trichomonas vaginalis</it>.</p> <p>Results</p> <p>We have identified the previously 'missing' <it>Giardia </it>RNase MRP RNA, which is a key ribozyme involved in pre-rRNA processing. We have also uncovered 18 new H/ACA box snoRNAs, expanding our knowledge of the H/ACA family of snoRNAs.</p> <p>Conclusions</p> <p>Results indicate that <it>Giardia intestinalis </it>and <it>Trichomonas vaginalis</it>, like their distant multicellular relatives, contain a rich infrastructure of RNA-based processing. From here we can investigate the evolution of RNA processing networks in eukaryotes.</p

Evolutionary Modeling and Prediction of Non-Coding RNAs in Drosophila

We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3′ end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA

Expression and Processing of a Small Nucleolar RNA from the Epstein-Barr Virus Genome

Small nucleolar RNAs (snoRNAs) are localized within the nucleolus, a sub-nuclear compartment, in which they guide ribosomal or spliceosomal RNA modifications, respectively. Up until now, snoRNAs have only been identified in eukaryal and archaeal genomes, but are notably absent in bacteria. By screening B lymphocytes for expression of non-coding RNAs (ncRNAs) induced by the Epstein-Barr virus (EBV), we here report, for the first time, the identification of a snoRNA gene within a viral genome, designated as v-snoRNA1. This genetic element displays all hallmark sequence motifs of a canonical C/D box snoRNA, namely C/C′- as well as D/D′-boxes. The nucleolar localization of v-snoRNA1 was verified by in situ hybridisation of EBV-infected cells. We also confirmed binding of the three canonical snoRNA proteins, fibrillarin, Nop56 and Nop58, to v-snoRNA1. The C-box motif of v-snoRNA1 was shown to be crucial for the stability of the viral snoRNA; its selective deletion in the viral genome led to a complete down-regulation of v-snoRNA1 expression levels within EBV-infected B cells. We further provide evidence that v-snoRNA1 might serve as a miRNA-like precursor, which is processed into 24 nt sized RNA species, designated as v-snoRNA124pp. A potential target site of v-snoRNA124pp was identified within the 3′-UTR of BALF5 mRNA which encodes the viral DNA polymerase. V-snoRNA1 was found to be expressed in all investigated EBV-positive cell lines, including lymphoblastoid cell lines (LCL). Interestingly, induction of the lytic cycle markedly up-regulated expression levels of v-snoRNA1 up to 30-fold. By a computational approach, we identified a v-snoRNA1 homolog in the rhesus lymphocryptovirus genome. This evolutionary conservation suggests an important role of v-snoRNA1 during γ-herpesvirus infection

The non-coding transcriptome as a dynamic regulator of cancer metastasis.

Since the discovery of microRNAs, non-coding RNAs (NC-RNAs) have increasingly attracted the attention of cancer investigators. Two classes of NC-RNAs are emerging as putative metastasis-related genes: long non-coding RNAs (lncRNAs) and small nucleolar RNAs (snoRNAs). LncRNAs orchestrate metastatic progression through several mechanisms, including the interaction with epigenetic effectors, splicing control and generation of microRNA-like molecules. In contrast, snoRNAs have been long considered "housekeeping" genes with no relevant function in cancer. However, recent evidence challenges this assumption, indicating that some snoRNAs are deregulated in cancer cells and may play a specific role in metastasis. Interestingly, snoRNAs and lncRNAs share several mechanisms of action, and might synergize with protein-coding genes to generate a specific cellular phenotype. This evidence suggests that the current paradigm of metastatic progression is incomplete. We propose that NC-RNAs are organized in complex interactive networks which orchestrate cellular phenotypic plasticity. Since plasticity is critical for cancer cell metastasis, we suggest that a molecular interactome composed by both NC-RNAs and proteins orchestrates cancer metastasis. Interestingly, expression of lncRNAs and snoRNAs can be detected in biological fluids, making them potentially useful biomarkers. NC-RNA expression profiles in human neoplasms have been associated with patients' prognosis. SnoRNA and lncRNA silencing in pre-clinical models leads to cancer cell death and/or metastasis prevention, suggesting they can be investigated as novel therapeutic targets. Based on the literature to date, we critically discuss how the NC-RNA interactome can be explored and manipulated to generate more effective diagnostic, prognostic, and therapeutic strategies for metastatic neoplasms